AI in Supply Chain Management

Enterprises working along the supply chain today are heavily dependent on their far-reaching network of suppliers and partners to keep goods moving efficiently. To achieve this, they need the right technology to incorporate strategic and sustainable considerations while also managing the various risks in such complex processes. There is also a massive opportunity for innovation to redefine how products are designed, created, and delivered to customers by incorporating artificial intelligence (AI) in their supply chain. AI can take care of workplace safety, support predictive maintenance, spot process inefficiencies and build intelligent supply chains that produce higher value and higher-quality products. AI’s ability to automate, augment and enhance customer experience and decision-making, and reinvent company strategies makes AI the holy grail of businesses that operate in the SCM space.

Supply Chain Management

Supply chain management is an umbrella term for supply chain, logistics, inventory management, and storage. All businesses that are part of a supply chain are linked by physical and information flows. Physical flows involve the transformation, transportation, and warehousing of goods and materials. They are the most evident and visible pieces of the supply chain. But just as important are information flows. Information flows allow various companies and divisions along the supply chain to coordinate their long-term plans and to control the daily flow of suppliers’ materials and goods up and down the supply chain.

How can AI be used in the supply chain?

The supply chain is a complex subject, and it consists of many smaller processes and interactions. The immense opportunities for artificial intelligence-driven supply chain management lay within these processes. Integrating machine learning (ML) in supply chain management can help automate many mundane tasks and allow companies to focus on more strategic and impactful business actions. Below you can find current possibilities and applications of artificial intelligence in the supply chain.

Enhance Human Workforces

Creating leaner manufacturing and warehousing rules is crucial for the supply chains, and automation can do much good here. AI can create a safer work environment, reduce repetitive tasks, reduce unsatisfying jobs, and increase productivity. Many warehouse and manufacturing-related activities are already automated, but introducing IoT-enabled devices powered with machine learning into these processes will vastly improve both speed and accuracy. AI systems can also solve several warehouse issues more quickly and accurately than humans can, simplify complex procedures, and speed up work. Furthermore, along with saving valuable time, AI-driven automation efforts can significantly reduce the need for and cost of manufacturing and warehouse personnel. Although it appears that AI is here to replace human labor, and some people might get scared, it is here to augment it, make it safer, and reduce the mundane parts. The leaders and managers must apply a people-first approach to become eager to adopt technologies that positively impact workers and their businesses.

Supply/Demand Forecasting

The enormous volumes of data that AI can manage make it useful for demand forecasting’s crucial activity. What AI offers is real-time, market-based demand forecasting that considers real-time data on sales, seasonal fluctuations, and abnormal demand patterns. AI and predictive analytics algorithms can make the supply chains leaner by forecasting inventory needs, including re-balancing across the network with continuous optimization based on the supply and demand. This information loop makes it possible to adjust the stocks and supplier planning. Thanks to AI technology, real-time information, planning, and distribution systems can be reconfigured to be proactive without waiting for specific order placement triggers.

Inventory Optimization (Turnover and Wastage)

It is inspiring to look at the supply chain model and see all the different, independent parties that make the global logistics network click. When you look up closer, though, there are places where you’ll see waste and unoptimized processes. Take food supply chains and the fact that approximately half of the food wastage occurs in the distribution stage. To ensure that all the orders that come in can be filled in without running out of certain items, the raw material suppliers, manufacturers, retailers, and wholesalers along the supply chain hold more inventory than they need as a safety margin. How can AI help here? AI can provide the aims for smart supply chain planning. Prescriptive analytics that considers supply and demand can aid more accurate planning, decrease waste, and cut costs.

Quality Control and Smart Maintenance

In the same way that detecting subtle trends aid in better supply chain planning, analyzing specific parameters with AI allows you to predict, anticipate and prevent quality issues. A typical example is that companies can introduce AI to promote a high level of precision in manufacturing using image analysis. A visual inspection straight on the production or assembly line can capture trends that could not be detected otherwise in many processes. The availability of high-res cameras, coupled with powerful image recognition technology, has dramatically cut the real-time in-line inspection cost. Additionally to image recognition, sensor-based processes used for product quality inspections bring uniformity and efficiency in quality control.

What are the examples of applying artificial intelligence in quality control assurance?

• an automated quality inspection enables the identification of defects in parts and finished products
• an automated inspection of assembly operations (e.g., missing or misplaced components)
• predict the quality of the product for given input materials, e.g., raw material supply, ingredients
• automated tracking and documentation of product quality
• manual intervention reduction and errors in quality checks and increase scale and scope of quality inspection
• reduced cost of quality assurance(less final control)
• preventive maintenance (e.g., spotting anomalies in how machines work and servicing before they break)

Advanced Network Analysis

Companies working along the supply chain generate enormous amounts of data (e.g., orders, suppliers status, manufacturing parameters, transportation details, etc.), and this trend will only continue to gain speed. External data points like weather and financial market indicators or social media data significantly impact the supply chain efficiency and turnover and cannot be ignored. However, the collected data is seldom leveraged to the extent that it could be. Deep learning models enable the machines to continually analyze the real-time data streams produced by these components, allowing them to implement immediate adjustments and improvements.

Augment and Enrich Data

The wide variation in data sets generated from the cameras and IoT sensors, telematics, logistics, and transportation systems have the potential to deliver the most value to improving supply chains by using artificial intelligence. Applying ML algorithms and techniques to improve supply chains starts with data sets with tremendous variability. Small details like changes in order frequency, delivery vehicle routes, scheduling trends, and more can be spotted, analyzed, and planned for quickly. Artificial Intelligence models trained on historical data together with external data are great for spotting patterns or trends. No matter how small the trend may be, the artificial intelligence platform can spot it and help businesses make better supply chain management decisions.

Shipping Efficiency

Faster and accurate transportation and on-time deliveries are the inevitable positive results of introducing smart technology solutions along the global supply chains. Artificial intelligence systems can help reduce dependency on manual efforts, making the entire logistics process faster and more reliable. The most challenging issues supply chain management faces are often found in optimizing logistics, e.g., to ensure materials needed to complete a production arrive on time.

Smart planning and predictions based on various data sources help facilitate timely delivery to the customer. AI-based systems accelerate traditional warehouse procedures, therefore removing operational bottlenecks along the value chain with minimal effort to achieve delivery targets.

What are the benefits of AI in the supply chain context?

Implementing artificial intelligence smartly can deliver several tangible effects in areas, such as:

Informed decision making

• Help your company in the decision making and leaner supply chain planning by providing operational information and insights about patterns and exceptions; support your employees with predictive analytics and forecasts to build new strategies and implementing data-based decisions.

Increased efficiency

• Save time and automate your employees’ mundane, repetitive tasks using AI and cognitive services; spot malfunctions before they even occur. Speed up logistics operations by spotting bottlenecks and finding automation solutions.

Competitive advantage

• Leverage data and analytics to build resilience while staying one step ahead of your competitors: recognize new opportunities and emerging new business models, optimize your supply chain systems and operations.

Scaling organization

• Enable company growth and scale your business by automating operations using AI. AI and machine learning applications make it possible to expand for global markets.

Customer satisfaction

• Increase your customers’ satisfaction by streamlining the delivery process and making your product accessible within 24h. Make the whole process transparent with status available at any time for your customers. With AI, you can speed up your response time by empowering human-computer interaction with chatbots and natural language processing.

Artificial Intelligence challenges in the supply chain

Like in other industries, AI adoption in the supply chain faces many challenges. It requires significant investments, organizational changes, transferring from legacy systems to prepare the IT infrastructure, and getting the data house in order. Some solutions can involve a substantial initial investment (money, time, ops) for one organization and multiple supply chain partners. To make AI work, organizations must pick the right problems and invest in developing and managing this emerging technology. It is important to note that the path to becoming AI-driven is unique for each business and depends upon the use case, available data, and operational processes in place. Companies that understand and anticipate the most common obstacles to implementing artificial intelligence and plan to deal with these obstacles will see AI’s positive ROI. The most common challenges for AI-based solutions implementation in supply chain management include:

• Wrong problem
• Wrong calculations for ROI of AI
• Not having the data (or not enough)
• Legacy infrastructure
• Organizational changes

Readying Your Supply Chain for Machine Learning

Companies increasingly understand that if they wish to unlock the real value of AI, they need to establish an agile, flexible data culture based on constant learning and improvement. What makes an AI implementation idea an ideal place to start? First of all, it needs to have an exact (and countable) business value. Secondly, it needs to be feasible. Feasibility can be divided into two various subjects: ease of implementation and data availability. We suggest approaching this initial phase with AI Design Sprint workshops. You already identified your next AI opportunity? Great! Should you dive into production implementation at this stage? Not really (especially if you want to be agile and keep the project budget in check). To succeed with AI, companies should start small by focusing on the research and experimentation phase with Proof of AI development. These experiments’ outcomes should be scaled up gradually and with caution, ensuring that each step incrementally moves the project and the organization towards AI adoption with confidence and exact business value. Next steps? To achieve scale, you need to bring the AI prototype up to speed with processing data in real-time from the production environment. The optimized model that works with real data can move onto production and, once deployed, the AI system can be used across multiple branches or factories. Reaching maturity here means continuous monitoring and optimizing for the generated value, output quality, and reliability.

Summary

There is a lot to learn about artificial intelligence and how it can improve supply chain management. As technology improves, data point numbers increase, and business needs change, there is no telling how much companies can accomplish with AI. When it comes to AI adoption, the question is no longer ‘why,’ but ‘when and how .’